






'''simple docstring'''







import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

__lowerCAmelCase        =							logging.getLogger(__name__)

__lowerCAmelCase        =							{"facebook/bart-base": BartForConditionalGeneration}
__lowerCAmelCase        =							{"facebook/bart-base": BartTokenizer}


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'   )
       parser.add_argument(
           '--validation_file'   ,       type=lowercase_   ,       default=lowercase_   ,       help='A csv or a json file containing the validation data.'   )
       parser.add_argument(
           '--max_length'   ,       type=lowercase_   ,       default=5   ,       help='The maximum total input sequence length after tokenization.'   ,       )
       parser.add_argument(
           '--num_beams'   ,       type=lowercase_   ,       default=lowercase_   ,       help=(
               'Number of beams to use for evaluation. This argument will be '
               'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
           )   ,       )
       parser.add_argument(
           '--model_name_or_path'   ,       type=lowercase_   ,       help='Path to pretrained model or model identifier from huggingface.co/models.'   ,       required=lowercase_   ,       )
       parser.add_argument(
           '--config_name'   ,       type=lowercase_   ,       default=lowercase_   ,       help='Pretrained config name or path if not the same as model_name'   ,       )
       parser.add_argument(
           '--device'   ,       type=lowercase_   ,       default='cpu'   ,       help='Device where the model will be run'   ,       )
       parser.add_argument('--output_file_path'   ,       type=lowercase_   ,       default=lowercase_   ,       help='Where to store the final ONNX file.'   )

       a_						     =			parser.parse_args()

       return args


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  int="cpu"   ):
       """simple docstring"""




       a_						     =			model_dict[model_name].from_pretrained(lowercase_   ).to(lowercase_   )
       a_						     =			tokenizer_dict[model_name].from_pretrained(lowercase_   )

       if model_name in ["facebook/bart-base"]:
              a_						     =			0
              a_						     =			None
              a_						     =			0

       return huggingface_model, tokenizer


def       __UpperCamelCase					(      lowercase_       :  str   ,       lowercase_       :  str   ,       lowercase_       :  List[str]   ,       lowercase_       :  Any   ,       lowercase_       :  Optional[int]   ):
       """simple docstring"""




       model.eval()

       a_						     =			None
       a_						     =			torch.jit.script(BARTBeamSearchGenerator(lowercase_   )   )

       with torch.no_grad():
              a_						     =			'My friends are cool but they eat too many carbs.'
              a_						     =			tokenizer([ARTICLE_TO_SUMMARIZE]   ,       max_length=1_024   ,       return_tensors='pt'   ).to(model.device   )

              a_						     =			model.generate(
                  inputs['input_ids']   ,       attention_mask=inputs['attention_mask']   ,       num_beams=lowercase_   ,       max_length=lowercase_   ,       early_stopping=lowercase_   ,       decoder_start_token_id=model.config.decoder_start_token_id   ,       )

              torch.onnx.export(
                  lowercase_   ,       (
                      inputs['input_ids'],
                      inputs['attention_mask'],
                      num_beams,
                      max_length,
                      model.config.decoder_start_token_id,
                  )   ,       lowercase_   ,       opset_version=14   ,       input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']   ,       output_names=['output_ids']   ,       dynamic_axes={
                      'input_ids': {0: 'batch', 1: 'seq'},
                      'output_ids': {0: 'batch', 1: 'seq_out'},
                  }   ,       example_outputs=lowercase_   ,       )

              logger.info('Model exported to {}'.format(lowercase_   )   )

              a_						     =			remove_dup_initializers(os.path.abspath(lowercase_   )   )

              logger.info('Deduplicated and optimized model written to {}'.format(lowercase_   )   )

              a_						     =			onnxruntime.InferenceSession(lowercase_   )
              a_						     =			ort_sess.run(
                  lowercase_   ,       {
                      'input_ids': inputs['input_ids'].cpu().numpy(),
                      'attention_mask': inputs['attention_mask'].cpu().numpy(),
                      'num_beams': np.array(lowercase_   ),
                      'max_length': np.array(lowercase_   ),
                      'decoder_start_token_id': np.array(model.config.decoder_start_token_id   ),
                  }   ,       )

              np.testing.assert_allclose(summary_ids.cpu().numpy()   ,       ort_out[0]   ,       rtol=1E-3   ,       atol=1E-3   )

              logger.info('Model outputs from torch and ONNX Runtime are similar.'   )
              logger.info('Success.'   )


def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			parse_args()
       a_						     =			5
       a_						     =			4

       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,       datefmt='%m/%d/%Y %H:%M:%S'   ,       level=logging.INFO   ,       )

       logger.setLevel(logging.INFO   )
       transformers.utils.logging.set_verbosity_error()

       a_						     =			torch.device(args.device   )

       a_     ,	a_						     =			load_model_tokenizer(args.model_name_or_path   ,       lowercase_   )

       if model.config.decoder_start_token_id is None:
              raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'   )

       model.to(lowercase_   )

       if args.max_length:
              a_						     =			args.max_length

       if args.num_beams:
              a_						     =			args.num_beams

       if args.output_file_path:
              a_						     =			args.output_file_path
       else:
              a_						     =			'BART.onnx'

       logger.info('Exporting model to ONNX'   )
       export_and_validate_model(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   )


if __name__ == "__main__":
  main()












'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




